docstr: Persist this model into the passed directory.
  
  Returns the metadata necessary to load the model again. In this case; `None`.
docstr_tok: ["Persist","this","model","into","the","passed","directory",".","Returns","the","metadata","necessary","to","load","the","model","again",".","In","this","case",";","`","None","`","."]
code: def persist(self) -> None:
      """
      Persist this model into the passed directory.
  
      Returns the metadata necessary to load the model again. In this case; `None`.
      """
      with self._model_storage.write_to(self._resource) as model_dir:
          dump(self.tfm, model_dir / "tfidfvectorizer.joblib")
  
imports: import logging
  from typing import Any, Text, Dict, List, Type
  from sklearn.feature_extraction.text import TfidfVectorizer
  from rasa.engine.recipes.default_recipe import DefaultV1Recipe
  from rasa.engine.graph import ExecutionContext, GraphComponent
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.storage import ModelStorage
  from rasa.nlu.featurizers.sparse_featurizer.sparse_featurizer import SparseFeaturizer
  from rasa.nlu.tokenizers.tokenizer import Tokenizer
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.shared.nlu.training_data.features import Features
  from rasa.shared.nlu.training_data.message import Message
  from rasa.nlu.constants import DENSE_FEATURIZABLE_ATTRIBUTES, FEATURIZER_CLASS_ALIAS
  from joblib import dump, load
  from rasa.shared.nlu.constants import TEXT, TEXT_TOKENS, FEATURE_TYPE_SENTENCE, FEATURE_TYPE_SEQUENCE
label_desc: Use image node_id 10 for calling the TfIdfFeaturizer obj's underlying member method code with example usage: obj.persist() without return types